by jsendak | Jan 1, 2024 | Computer Science
Expert Commentary
Multi-object tracking (MOT) is a challenging task in computer vision, where the goal is to estimate the trajectories of multiple objects over time. It has numerous applications in various fields, including surveillance, autonomous vehicles, and robotics. In this article, the authors address the problem of multi-object smoothing, where the object detections can be conditioned on all the measurements in a given time window.
Traditionally, Bayesian methods have been widely used for multi-object tracking and have achieved good results. However, the computational complexity of these methods increases exponentially with the number of objects being tracked, making them infeasible for large-scale scenarios.
To overcome this issue, the authors propose a deep learning (DL) based approach specifically designed for scenarios where accurate multi-object models are available and measurements are low-dimensional. Their proposed DL architecture separates the data association task from the smoothing task, which allows for more efficient and accurate tracking.
This is an exciting development as deep learning has shown great potential in various computer vision tasks. By leveraging deep neural networks, the proposed method is able to learn complex patterns from data and make more accurate predictions.
The authors evaluate their proposed approach against state-of-the-art Bayesian trackers and DL trackers in various tasks of varying difficulty. This comprehensive evaluation provides valuable insights into the performance of different methods in the multi-object tracking smoothing problem setting.
Overall, this research introduces a novel DL architecture tailored for accurate multi-object tracking, addressing the limitations of existing Bayesian trackers. It opens up possibilities for improved performance and scalability in complex multi-object tracking scenarios. Further research could focus on refining the proposed DL architecture and conducting experiments on more diverse datasets to assess its generalizability.
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by jsendak | Jan 1, 2024 | Computer Science
Nowadays neural-network-based image- and video-quality metrics show better
performance compared to traditional methods. However, they also became more
vulnerable to adversarial attacks that increase metrics’ scores without
improving visual quality. The existing benchmarks of quality metrics compare
their performance in terms of correlation with subjective quality and
calculation time. However, the adversarial robustness of image-quality metrics
is also an area worth researching. In this paper, we analyse modern metrics’
robustness to different adversarial attacks. We adopted adversarial attacks
from computer vision tasks and compared attacks’ efficiency against 15
no-reference image/video-quality metrics. Some metrics showed high resistance
to adversarial attacks which makes their usage in benchmarks safer than
vulnerable metrics. The benchmark accepts new metrics submissions for
researchers who want to make their metrics more robust to attacks or to find
such metrics for their needs. Try our benchmark using pip install
robustness-benchmark.
Deep Analysis of Neural-Network-Based Image- and Video-Quality Metrics
In recent years, neural-network-based image- and video-quality metrics have shown remarkable advancements in terms of performance compared to traditional methods. However, with this progress comes an increased vulnerability to adversarial attacks that can manipulate the scores of these metrics without actually improving the visual quality. In this multidisciplinary study, we investigate the robustness of modern metrics against various adversarial attacks.
The field of multimedia information systems encompasses various domains such as computer vision, machine learning, and human-computer interaction. Understanding the performance and vulnerabilities of image- and video-quality metrics is crucial for developing reliable multimedia systems that can accurately assess the visual quality of images and videos.
Animations, artificial reality, augmented reality, and virtual realities are all interconnected with multimedia information systems. These technologies heavily rely on accurate assessment and manipulation of visual content. Therefore, it is essential to evaluate the robustness of quality metrics in these areas to ensure a seamless user experience.
In our comprehensive analysis, we compared the efficiency and resilience of 15 state-of-the-art no-reference image/video-quality metrics against adversarial attacks derived from computer vision tasks. By subjecting these metrics to various attacks, we gained valuable insights into their susceptibility and possible vulnerabilities.
Interestingly, some metrics exhibited high resistance to adversarial attacks, making them safer choices for benchmarking purposes. These robust metrics can provide reliable and consistent assessments of image and video quality even in the presence of adversarial manipulation.
Our benchmark framework offers researchers a platform to submit their own metrics, allowing them to enhance the robustness of their models against adversarial attacks or identify suitable metrics for their specific requirements. Using pip install robustness-benchmark, researchers can easily access and utilize this benchmark for their experiments and studies.
In conclusion, this study highlights the importance of examining the adversarial robustness of neural-network-based image- and video-quality metrics. By analyzing their vulnerabilities, we can improve the reliability and accuracy of multimedia systems and ensure a seamless user experience across various domains such as animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Jan 1, 2024 | Computer Science
Expert Commentary: Enhancing Object Detection in LiDAR Point Clouds with TimePillars
Object detection in LiDAR point clouds is a crucial task in robotics and especially in autonomous driving. In this field, single frame methods have been widely used, leveraging the information from individual sensor scans. These approaches have shown good performance in terms of accuracy, while maintaining relatively low inference time.
However, one limitation of these single frame methods is their struggle with long-range detection. For example, detecting objects at distances of 200m or more is particularly challenging. This long-range detection capability is essential for achieving safe and efficient automation in autonomous vehicles.
One approach to address this limitation is to aggregate multiple sensor scans to form denser point cloud representations. By doing so, the system gains time-awareness and is able to capture information about how the environment is changing over time. This approach, however, often requires problem-specific solutions that involve extensive data processing and may not meet real-time runtime requirements.
Introducing TimePillars, a temporally-recurrent object detection pipeline, aims to overcome these challenges. The proposed pipeline leverages the pillar representation of LiDAR data across time, taking into consideration hardware integration efficiency constraints. The research team behind TimePillars also benefited from the diversity and long-range information provided by the novel Zenseact Open Dataset (ZOD) during their experimentation.
In their study, the researchers demonstrate the advantages of incorporating recurrency into the object detection pipeline. They show that even basic building blocks can achieve robust and efficient results when leveraging temporal information. This finding suggests that incorporating time-awareness into object detection algorithms can significantly improve their performance.
By using TimePillars, researchers and developers can potentially overcome the limitations of single frame methods in long-range object detection. The approach offers a promising solution that combines the benefits of dense point cloud representations and time-awareness, without compromising runtime requirements. With further advancements and optimizations, TimePillars could contribute to enhancing the safety and efficiency of autonomous driving systems.
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by jsendak | Jan 1, 2024 | Computer Science
Diffusion models have transformed the image-to-image (I2I) synthesis and are
now permeating into videos. However, the advancement of video-to-video (V2V)
synthesis has been hampered by the challenge of maintaining temporal
consistency across video frames. This paper proposes a consistent V2V synthesis
framework by jointly leveraging spatial conditions and temporal optical flow
clues within the source video. Contrary to prior methods that strictly adhere
to optical flow, our approach harnesses its benefits while handling the
imperfection in flow estimation. We encode the optical flow via warping from
the first frame and serve it as a supplementary reference in the diffusion
model. This enables our model for video synthesis by editing the first frame
with any prevalent I2I models and then propagating edits to successive frames.
Our V2V model, FlowVid, demonstrates remarkable properties: (1) Flexibility:
FlowVid works seamlessly with existing I2I models, facilitating various
modifications, including stylization, object swaps, and local edits. (2)
Efficiency: Generation of a 4-second video with 30 FPS and 512×512 resolution
takes only 1.5 minutes, which is 3.1x, 7.2x, and 10.5x faster than CoDeF,
Rerender, and TokenFlow, respectively. (3) High-quality: In user studies, our
FlowVid is preferred 45.7% of the time, outperforming CoDeF (3.5%), Rerender
(10.2%), and TokenFlow (40.4%).
Analysis of Video-to-Video Synthesis Framework
The content discusses the challenges in video-to-video (V2V) synthesis and introduces a novel framework called FlowVid that addresses these challenges. The key issue in V2V synthesis is maintaining temporal consistency across video frames, which is crucial for creating realistic and coherent videos.
FlowVid tackles this challenge by leveraging both spatial conditions and temporal optical flow clues within the source video. Unlike previous methods that rely solely on optical flow, FlowVid takes into account the imperfection in flow estimation and encodes the optical flow by warping from the first frame. This encoded flow serves as a supplementary reference in the diffusion model, enabling the synthesis of videos by propagating edits made to the first frame to successive frames.
One notable aspect of FlowVid is its multi-disciplinary nature, as it combines concepts from various fields including computer vision, image synthesis, and machine learning. The framework integrates techniques from image-to-image (I2I) synthesis and extends them to videos, showcasing the potential synergy between these subfields of multimedia information systems.
In the wider field of multimedia information systems, video synthesis plays a critical role in applications such as visual effects, virtual reality, and video editing. FlowVid’s ability to seamlessly work with existing I2I models allows for various modifications, including stylization, object swaps, and local edits. This makes it a valuable tool for artists, filmmakers, and content creators who rely on video editing and manipulation techniques to achieve their desired visual results.
Furthermore, FlowVid demonstrates efficiency in video generation, with a 4-second video at 30 frames per second and 512×512 resolution taking only 1.5 minutes. This speed is significantly faster compared to existing methods such as CoDeF, Rerender, and TokenFlow, highlighting the potential impact of FlowVid in accelerating video synthesis workflows.
The high-quality results achieved by FlowVid, as evidenced by user studies where it was preferred 45.7% of the time over competing methods, validate the effectiveness of the proposed framework. This indicates that FlowVid successfully addresses the challenge of maintaining temporal consistency in V2V synthesis, resulting in visually pleasing and realistic videos.
In conclusion, the video-to-video synthesis framework presented in the content, FlowVid, brings together concepts from various disciplines to overcome the challenge of temporal consistency. Its integration of spatial conditions and optical flow clues demonstrates the potential for advancing video synthesis techniques. Additionally, its relevance to multimedia information systems, animations, artificial reality, augmented reality, and virtual realities highlights its applicability in diverse industries and creative endeavors.
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by jsendak | Jan 1, 2024 | Computer Science
Expert Commentary: Machine Learning for Automating Cockpit Gauge Reading
This research paper focuses on utilizing machine learning techniques, specifically Convolutional Neural Networks (CNNs), to automate the reading of cockpit gauges. The goal is to extract relevant data and infer aircraft states from instrument images, ultimately reducing the workload on pilots and enhancing flight safety.
One of the significant contributions of this research is the introduction of a method to invert affine transformations applied to the instrument images. Affine transformations include rotations, translations, and scaling, which can complicate the analysis. By training a CNN on synthetic images with known transformations, the researchers were able to deduce and compensate for these transformations when presented with real-world instrument images.
Furthermore, the researchers propose a technique called the “Clean Training Principle.” This approach focuses on generating datasets from a single image to ensure optimal noise-free training. By augmenting the dataset with transformed variations of a single image, they can train the CNN to be robust against different orientations, lighting conditions, and other factors that may introduce noise into the input data.
Additionally, the paper introduces CNN interpolation as a means to predict continuous values from categorical data. In the context of cockpit gauges, this interpolation can provide accurate estimations of aircraft states such as airspeed and altitude, which are typically represented by categorical indicators. This technique expands the potential applications of CNNs in aviation, offering possibilities for extracting more detailed information from limited input sources.
The research also touches upon hyperparameter optimization and software engineering considerations for implementing machine learning systems in real-world scenarios. Hyperparameters play a crucial role in CNN performance, and finding the optimal values can significantly impact accuracy and robustness. Additionally, the paper emphasizes the importance of developing reliable ML system software that can handle real-time data processing and seamless integration with existing cockpit systems.
Overall, this paper presents valuable insights and techniques for automating cockpit gauge reading using machine learning. Future research in this area could delve deeper into other types of cockpit instruments and explore ways to adapt the proposed methods for real-time applications in operational aircraft. By combining advancements in AI with aviation, there is potential for significant improvements in flight safety and pilot efficiency.
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by jsendak | Jan 1, 2024 | Computer Science
The quality of a face crop in an image is decided by many factors such as
camera resolution, distance, and illumination condition. This makes the
discrimination of face images with different qualities a challenging problem in
realistic applications. However, most existing approaches are designed
specifically for high-quality (HQ) or low-quality (LQ) images, and the
performances would degrade for the mixed-quality images. Besides, many methods
ask for pre-trained feature extractors or other auxiliary structures to support
the training and the evaluation. In this paper, we point out that the key to
better understand both the HQ and the LQ images simultaneously is to apply
different learning methods according to their qualities. We propose a novel
quality-guided joint training approach for mixed-quality face recognition,
which could simultaneously learn the images of different qualities with a
single encoder. Based on quality partition, classification-based method is
employed for HQ data learning. Meanwhile, for the LQ images which lack identity
information, we learn them with self-supervised image-image contrastive
learning. To effectively catch up the model update and improve the
discriminability of contrastive learning in our joint training scenario, we
further propose a proxy-updated real-time queue to compose the contrastive
pairs with features from the genuine encoder. Experiments on the low-quality
datasets SCface and Tinyface, the mixed-quality dataset IJB-B, and five
high-quality datasets demonstrate the effectiveness of our proposed approach in
recognizing face images of different qualities.
Improving Mixed-Quality Face Recognition with Quality-Guided Joint Training
In the field of multimedia information systems, face recognition has always been a challenging problem, particularly when dealing with mixed-quality face images. The quality of a face crop in an image is influenced by various factors, including camera resolution, distance, and illumination condition. Discriminating face images with different qualities poses a difficult task in realistic applications.
Traditional approaches to face recognition have been designed specifically for either high-quality (HQ) or low-quality (LQ) images. However, when applied to mixed-quality images, these approaches tend to perform poorly. Moreover, many existing methods require pre-trained feature extractors or auxiliary structures to support training and evaluation.
In this paper, the authors propose a novel quality-guided joint training approach for mixed-quality face recognition. The key idea is to apply different learning methods based on the qualities of the images. This approach enables simultaneous learning of HQ and LQ images using a single encoder.
For HQ data learning, a classification-based method is employed based on quality partitioning. This allows for better understanding and interpretation of HQ images. On the other hand, LQ images lack identity information, so the authors propose learning them using self-supervised image-image contrastive learning.
To address the challenge of model update and improve the discriminability of contrastive learning in the joint training scenario, the authors propose a proxy-updated real-time queue. This queue is used to compose contrastive pairs with features from the genuine encoder. This ensures that the model keeps up with updates and enhances the effectiveness of contrastive learning.
The proposed approach is evaluated using various datasets, including low-quality datasets such as SCface and Tinyface, a mixed-quality dataset called IJB-B, and five high-quality datasets. The experiments demonstrate the effectiveness of the proposed approach in recognizing face images of different qualities.
Multi-disciplinary Nature and Related Concepts
This research on mixed-quality face recognition combines concepts and techniques from various disciplines. It leverages principles from computer vision, machine learning, and multimedia information systems to address the challenge of discriminating face images with different qualities.
Furthermore, this study is closely related to the broader field of multimedia information systems, as it deals with the analysis and understanding of visual content, specifically face images. It incorporates techniques for image quality assessment, feature extraction, and learning methods to improve the recognition of face images of different qualities.
In addition, the proposed approach has implications for animations, artificial reality, augmented reality, and virtual realities. Face recognition is a fundamental component in these domains, and advancements in mixed-quality face recognition can enhance the realism and accuracy of facial animations and virtual environments. By applying different learning methods according to image qualities, the proposed approach contributes to improving the overall quality and fidelity of multimedia systems involving virtual representations of human faces.
Overall, this research presents a novel quality-guided joint training approach for mixed-quality face recognition. It demonstrates the importance of considering different learning methods based on image qualities to achieve better performance. With its multidisciplinary nature and relevance to multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, this study opens up new possibilities for advancing face recognition technologies and enhancing various applications in visual computing.
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